Hi Emma,

I know it's been a long time, but my RA recently explored this and we think that mri_aparc2aseg might be a better option that mri_label2vol. Anyway, here's the updated process for mapping the Yeo2011 networks from fsaverage5 to individual volumetric space:

1) Transform from fsaverage to subject's surface

mri_surf2surf --srcsubject fsaverage5 --trgsubject yoursubject --hemi lh --sval-annot path_to/lh.Yeo2011_17Networks_N1000.annot –tval $SUBJECTS_DIR/yoursubject/label/lh.Yeo_17Network_native.annot

(repeat for rh)

2) Transform from subject's surface into your subject's volume

Instead of using "mri_label2vol", we now recommend using "mri_aparc2aseg". This will give you ROIs that fully cover subject's grey matter.

mri_aparc2aseg --s yoursubject --annot Yeo_17Network_native --o $SUBJECTS_DIR/yoursubject/mri/outputfile.mgz

One thing to note: "mri_aparc2aseg" will automatically add 1000 for left cerebral cortex voxels and add 2000 for right cerebral cortex voxels. Therefore, for example, a left hemisphere voxel in network 5 will have a value of 1005 in your outputfile.mgz. Here is our suggestion:

a) Find the indices of left/right cerebral cortex by extracting the voxels with a value of 3 or 42 in the subject's aseg.

b) Use those indices to mask out the voxels that are not cerebral cortex in your outputfile.mgz file (i.e. set non cerebral cortical voxels to be 0).

c) For the cortical voxels, you may subtract 1000 for those between 1000 and 2000; and subtract 2000 for those greater than 2000.

Thanks,

Thomas

On Wed, Feb 15, 2017 at 11:52 AM Thomas Yeo <ythomas@csail.mit.edu> wrote:

Hi Emma,

I recommend that you follow the mri_surf2surf and mri_label2vol route.
As recommended in the other emails, you should probably load the
resulting parcellations and make sure they look ok.

As far as I know, "mris_ca_train" and "mris_ca_label" are not quite so
applicable here because they require multiple subjects with
resting-state parcellations in order to train the classifier. We are
working on an individual subject 7-network and 17-network parcellation
algorithm, but it's not ready yet.

What kind of edits have you already done? If your edits involve
correcting the gray/white matter segmentation and the cortical
surfaces, then your edits should be reflected (since mri_surf2surf and
mri_label2vol should utilized the updated cortical surfaces). However,
if your edits involving editing the Desikan's parcellation itself,
then probably not.

Regards,
Thomas

On Wed, Feb 15, 2017 at 6:11 AM, Bailin, Emma
<Emma_Bailin@meei.harvard.edu> wrote:
> Hello,
>
>
>
> I’m preparing to do resting state analyses and I’d like to use the Yeo 2011
> network atlas for the cortical parcellations. Looking through the archive, I
> know that mri_surf2surf with the –sval-annot flag can be used to convert the
> fsaverage from the Yeo et al 2011 study to an individual subject space, but
> I noticed a caveat to this when I called mri_surf2surf –help. The caveat is
> on example 5: “this is not a substitute for running the cortical
> parcellation! The parcellations that it maps to the new subject may not be
> appropriate for that subject.”
>
>
>
> Given this information, I’ve got a few questions:
>
>
>
> 1) How accurate is the mapping using mri_surf2surf? That is, is it good
> for preliminary data, but not for the final analysis?
>
> 2) How do you run the cortical parcellation using the Yeo 2011 atlas?
> [I know that mris_ca_train and mris_ca_label exist, but I’m unsure if they
> are the right commands to run when I’m not building my own atlas]
>
> 3) If I do the cortical parcellations using the Yeo 2011 atlas, will
> the edits/labels I’ve done using the Desikan atlas remain accurate? This
> goes back to the second question, as I’m not sure where in the general
> processing stage I need to go.
>
>
>
> Thank you!
>
>
>
> Sincerely,
>
>
>
> Emma Bailin
>
>
>
> Emma Bailin
>
> Research Coordinator
>
> Laboratory for Visual Neuroplasticity
>
> Schepens Eye Research Institute
>
> Mass. Eye and Ear Infirmary
>
> Harvard Medical School
>
>
>
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